A self-adaptive approach for curbstone/roadside detection based on human-like signal processing and multi-sensor fusion

Newly emerging, highly complex Advanced Driver Assistance Systems (ADAS) fuse the output of various system modules (e.g., lane detection, object classification). Such knowledge fusion is realized in order to gain additional information of the environment allowing for complex system tasks as path planning, the active search for specific objects and task-specific analysis of the environment. As part of our previous work, we realized a highly generic type of such ADAS using biological principles. The present contribution offers a novel approach for the detection of curbstones and elevated roadsides in inner-city that relies on biological principles taking inspiration from the human neural signal processing. The gathered results can be fused to an ADAS in order to improve the quality of various other system percepts and allow additional system tasks.

[1]  P. Lombardi,et al.  Unified stereovision for ground, road, and obstacle detection , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  Alberto BroggiDipartimento di Ingegneria dell ' InformazioneUniversit.  Robust real-time lane and road detection in critical shadow conditions , 1995 .

[3]  Jannik Fritsch,et al.  Enhancing Robustness of a Saliency-Based Attention System for Driver Assistance , 2008, ICVS.

[4]  Volker Willert,et al.  Non-Gaussian velocity distributions integrated over space, time, and scales , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Sebastian Thrun,et al.  Winning the DARPA Grand Challenge , 2006, PKDD.

[6]  Roland Schulz,et al.  Laserscanner for Multiple Applications in Passenger Cars and Trucks , 2006 .

[7]  Jannik Fritsch,et al.  A Generic Temporal Integration Approach for Enhancing Feature-based Road-detection Systems , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[8]  Robert Kastner,et al.  Towards a proactive biologically-inspired Advanced Driver Assistance System , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[9]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[10]  Wang Rong-ben,et al.  Study on curb detection method based on 3D range image by LaserRadar , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[11]  Julian Eggert,et al.  Integrated Research and Development Environment for Real-Time Distributed Embodied Intelligent Systems , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  R. Danescu,et al.  A stereovision-based probabilistic lane tracker for difficult road scenarios , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[13]  H. Barlow.  Vision Science: Photons to Phenomenology by Stephen E. Palmer , 2000, Trends in Cognitive Sciences.

[14]  Sergiu Nedevschi,et al.  Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[15]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[16]  Jannik Fritsch,et al.  Adaptive multi-cue fusion for robust detection of unmarked inner-city streets , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[17]  Sergiu Nedevschi,et al.  Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[18]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[19]  Janne Heikkilä,et al.  A four-step camera calibration procedure with implicit image correction , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  M. Kleinehagenbrock,et al.  Towards a human-like vision system for Driver Assistance , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[21]  Ralph Trapp.  Stereoskopische Korrespondenzbestimmung mit impliziter Detektion von Okklusionen , 1998 .

[22]  R. Aufrere,et al.  Multiple sensor fusion for detecting location of curbs, walls, and barriers , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).